Safe energy efficiency optimization method and system, and medium

Abstract

The invention discloses a safety energy efficiency optimization method and system, and a medium. The method comprises the following steps: initializing flight parameters of an unmanned aerial vehicle; establishing a system channel model, an energy consumption model and a UAV safety energy efficiency maximization model according to the UAV flight parameters; and solving task allocation, a time allocation factor, terminal equipment transmitting power and an unmanned aerial vehicle flight path through an alternating iterative optimization algorithm until the energy efficiency convergence of the UAV-assisted MEC system is realized, and outputting an optimized safety energy efficiency maximization result. The safety rate of the system is maximized while the energy efficiency of the system is ensured, and the overall performance and safety of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment monitoring and control systems, and in particular to a safety energy efficiency optimization method, system and medium. Background Art

[0002] With the rapid development of the Internet of Things (IoT) and the fifth generation of wireless communication technology (5G), the demand for computing-intensive tasks by terminal devices (TDs) has exploded. However, due to the limited computing power and energy of terminal devices, it is difficult to independently handle complex computing tasks, resulting in reduced computing performance and quality of service (QoS) of the system. In order to improve computing power, Mobile Edge Computing (MEC) was proposed, which allows terminal devices to offload some computing tasks to servers deployed at the edge of the network, thereby reducing the computing burden and improving computing efficiency.

[0003] However, traditional MEC system servers are deployed in fixed locations, which are limited by transmission distance and deployment costs and cannot be flexibly adjusted according to the specific needs of terminal devices. In addition, in emergencies such as natural disasters or military attacks, ground MEC servers may be damaged, resulting in a scarcity of computing resources, which in turn reduces the performance of task offloading. Especially in some remote areas or emergency scenarios, the accessibility and computing power of traditional MEC systems are limited, and they cannot provide efficient computing services.

[0004] In recent years, Unmanned Aerial Vehicle (UAV)-assisted MEC system has become a new solution. With its high maneuverability and line-of-sight (LoS) transmission characteristics, UAV can be quickly deployed and provide temporary computing services for terminal devices. Compared with traditional fixed MEC systems, UAV-assisted MEC systems have flexible task scheduling capabilities and can allocate resources according to demand in different scenarios, significantly improving computing and communication efficiency. However, due to the limited computing power and energy of UAV, its computing power and communication coverage are also limited, which poses a higher challenge to improving the performance of the system.

[0005] In addition, since the UAV communicates with the terminal device through a wireless link, the UAV-assisted MEC system also faces the problem of communication security. During the communication process, due to the openness of the wireless channel, potential eavesdroppers (Eve) may steal sensitive information by monitoring wireless signals, thus causing serious security risks. Therefore, how to improve the security of the communication link by optimizing the task allocation and energy consumption of the UAV while meeting the computing task requirements of the terminal device has become the focus of current research.

[0006] In order to improve the service quality of TDs, UAVs should maximize the use of resources and trajectory optimization security computing capabilities within a limited service time, and minimize the energy consumed by UAVs to maximize energy efficiency, which is of great significance for improving the performance of UAV-assisted MEC communication systems. Summary of the invention

[0007] The purpose of the present invention is to provide a safety energy efficiency optimization method, system, and storage medium to maximize the safety rate of the system while ensuring the energy efficiency of the system, thereby improving the overall performance and safety of the system.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The safety energy efficiency optimization method comprises the following steps:

[0010] Initialize the UAV flight parameters;

[0011] Establishing a system channel model, an energy consumption model and a UAV safety energy efficiency maximization model according to the UAV flight parameters;

[0012] The task allocation, time allocation factor, terminal device transmission power and UAV flight trajectory are solved through an alternating iterative optimization algorithm until the UAV safety and energy efficiency converges, and the optimized UAV safety and energy efficiency maximization result is output.

[0013] In the above technical solution, establishing a system channel model includes:

[0014] Determine the channel power gain between the UAV and the terminal device, and the channel power gain between the terminal device and the ground eavesdropper;

[0015] Define the time allocation factor and determine the constraints of the time allocation factor;

[0016] Calculate the rate between the terminal device and the drone, the rate between the terminal device and the ground eavesdropper, and the confidentiality rate, and build a system channel model.

[0017] In the above technical solution, establishing a system energy consumption model includes:

[0018] Determine the mission energy consumption, wherein the mission energy consumption includes computing mission energy consumption, communication mission energy consumption, and flight propulsion energy consumption;

[0019] The energy consumption constraints of each task are determined according to the energy consumption of the task, and the energy consumption model required for the UAV transmission power, task allocation and flight trajectory optimization is constructed according to the energy consumption constraints.

[0020] In the above technical solution, a UAV safety energy efficiency maximization model is established, including:

[0021] Define time allocation factor, task allocation, transmission power and UAV flight trajectory as block variables;

[0022] The system energy consumption model and the channel model are combined to maximize the energy efficiency of the UAV by optimizing the transmission power, task offloading decision and flight trajectory under the constraints of the system energy consumption model; the energy efficiency of the UAV is the ratio between the terminal device's safe offloading of data and the energy consumption of the UAV during the flight time.

[0023] In the above technical solution, task allocation, time allocation factor, terminal device transmission power and UAV flight trajectory are solved by alternating iterative optimization algorithm, including:

[0024] The first stage optimizes the time allocation factor and task allocation, given the trajectory and transmission power of the UAV;

[0025] The second stage optimizes the transmit power by a continuous convex approximation method;

[0026] The third stage optimizes the UAV flight trajectory and introduces relaxation variables.

[0027] In the above technical solution, through the time allocation factor and task allocation in the first stage and the transmission power in the second stage, slack variables are introduced into the UAV confidentiality rate and flight energy consumption, and the third stage problem is solved by the continuous convex approximation method to optimize the UAV trajectory.

[0028] In the above technical solution, the safe rate convergence of the MEC system assisted by drones includes:

[0029] The difference between the maximum energy efficiency of the current iteration and the maximum energy efficiency of the previous iteration is less than the preset convergence value, or the maximum number of iterations is reached.

[0030] The present invention provides a drone control system, comprising

[0031] Rotary wing drones;

[0032] at least one ground terminal device;

[0033] Mobile edge computing server;

[0034] A control unit is used to implement any of the safety energy efficiency optimization methods described above.

[0035] The present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the safety energy efficiency optimization method as described in any one of the above.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] This application provides a security energy efficiency optimization method, system, and medium, which jointly optimizes the task allocation, time allocation factor, terminal transmission power, and flight trajectory of the UAV to maximize the UAV security energy efficiency under the constraints of UAV energy consumption and system confidentiality rate. In a multi-task offloading environment, communication security and energy efficiency are optimized at the same time to improve the overall performance of the communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a system structure diagram provided by an embodiment of the present invention.

[0039] Figure 2 The present invention is a flowchart of an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Embodiment 1:

[0042] This embodiment provides a security energy efficiency optimization method, which jointly optimizes the unloading time allocation factor, the UAV trajectory, the computing task allocation and the transmission power of the terminal device, and establishes a UAV security energy efficiency maximization model under the constraints of satisfying the confidentiality unloading constraints, the energy consumption of the terminal device and the UAV. It not only optimizes the energy consumption of the UAV, but also optimizes the system confidentiality unloading rate, improves the service quality of the application, expands the computing power of the terminal device, and improves the performance of the communication system.

[0043] The present invention firstly aims at the fact that TDs with limited energy and computing power cannot meet the low-latency communication and computing requirements of applications, and in the presence of ground eavesdroppers, it is necessary to meet the computing-intensive and delay-sensitive task requirements and ensure the security of the communication system to complete the total computing task. The system model is as follows Figure 1As shown in the figure, one of the UAVs equipped with a MEC server starts from a fixed starting point and flies to the end point after passing through K ground TDs. TDs can offload part of the computing tasks to the UAV equipped with a MEC server in the air, and the entire computing task processing needs to be completed within the UAV flight time T. TDs can calculate tasks by themselves, or offload part of the computing tasks to the UAV during the flight of the UAV. In addition, the present invention also takes into account the existence of potential eavesdroppers on the ground, and the specific location of the eavesdroppers is unknown.

[0044] The goal of the present invention is to maximize the UAV safety energy efficiency under the constraints of UAV energy consumption and system confidentiality rate by jointly optimizing the time allocation factor, the computational task allocation of TDs, the transmission power of TDs, and the UAV flight trajectory.

[0045] Specifically, the method comprises the following steps:

[0046] Step S1: Initialize the flight parameters of the drone. Specifically, input the position w of each ground TDs k ={w1,w2,...,w k}, k∈K={1,2,...,K}, UAV starting point q I With the end point q F and the estimated position w of the ground eavesdropper e . Assume that the UAV maintains a constant altitude H during the flight. During the entire flight of the UAV, the flight time T is evenly divided into N (n∈N={1,2,...,N}) time slots, and the duration of each time slot is δ=T / N. The total amount of tasks that the kth TDs needs to complete is L k , channel power gain β0 at reference distance d = 1m, communication bandwidth B, noise power σ at the receiver 2 , UAV blade power P in hovering state o and induced power P i , U tip is the rotor blade tip speed, v o represents the average rotor induced speed in forward flight, d o and s represent the fuselage drag ratio and rotor solidity, respectively, and ε and M represent the air density and rotor disc area. The path loss index is The UAV calculates the number of central processing unit (CPU) cycles C for each task u , the energy consumption of each CPU cycle of UAV is expressed as P u , UAV’s CPU calculation frequency f u , the total energy of UAV is E u , UAV maximum speed v max , the effective capacitance coefficient κ of the UAV processor chip architecture u The total energy of the kth TDs is Ek , the kth TDs calculates the number of cycles C of the central processing unit (CPU) of each task k , the effective capacitance coefficient κ of the kth TDs processor chip architecture k , the peak transmission power P of TDs max , the CPU calculation frequency f of the kth TDs k , the computational task that the kth TDs offloads to the UAV in the nth time slot is l k [n]. The flight speed of the UAV in the nth time slot is v u [n], initialize the transmission power of the kth TDs unloaded to the UAV in the nth time slot to P k [n], initialize the UAV trajectory corresponding to the nth time slot to q u [n].

[0047] Step S2: establishing a system channel model, an energy consumption model and a UAV safety energy efficiency maximization model according to the UAV flight parameters.

[0048] The construction of the system channel model is as follows:

[0049] Determine the channel power gain between the UAV and the terminal device, and the channel power gain between the terminal device and the ground eavesdropper;

[0050] Define the time allocation factor and determine the constraints of the time allocation factor;

[0051] Calculate the rate between the terminal device and the drone, the rate between the terminal device and the ground eavesdropper, and the confidentiality rate, and build a system channel model.

[0052] Among them, the channel power gains between TDs and UAV, and the channel power gains between TDs and ground eavesdroppers are:

[0053]

[0054] Where Δw e =[Δx e ,Δy e ] represents the estimation error, and the estimation error satisfies Δx e 2 +Δy e 2 ≤Q e 2 , where Q e is the radius of the uncertainty circular region around Eve’s estimated position. ξ is the Rayleigh fading coefficient following a unit mean exponential distribution.

[0055] Considering the time division multiple access (TDMA) protocol to realize the computation offloading of UAV, the time slot n is further divided into K sub-time slots. The time allocated to the kth user in each time slot n is τ k [n], where τ k [n] represents the time allocation factor, which needs to meet the following constraints:

[0056]

[0057] The rate of the kth TDs with the drone in the nth time slot is expressed as:

[0058]

[0059] Since there is an eavesdropper on the ground, the rate of the kth TDs with the ground eavesdropper in the nth time slot is:

[0060]

[0061] The achievable confidentiality rate of the kth TDs with the UAV in the nth time slot is given by:

[0062]

[0063] in

[0064] The establishment of system energy consumption model includes:

[0065] Determine the mission energy consumption, wherein the mission energy consumption includes computing mission energy consumption, communication mission energy consumption, and flight propulsion energy consumption;

[0066] The energy consumption constraints of each task are determined according to the energy consumption of the task, and the energy consumption model required for the UAV transmission power, task allocation and flight trajectory optimization is constructed according to the energy consumption constraints.

[0067] In the computing process, to reduce the pressure of local computing, TDs can offload part of the computing tasks to the UAV equipped with MEC server for computing. k [n] is the computational task offloaded to the UAV by the kth TDs in the nth time slot and satisfies:

[0068]

[0069] The confidentiality rate of the kth TDs with the UAV in the nth time slot must not be lower than the unloading rate to ensure that the unloaded data bits are secure, that is:

[0070]

[0071] TDs offloads part of the computing tasks to UAV, and the computing energy consumed by UAV is:

[0072]

[0073] The local computing energy consumption and communication energy consumption of TDs are:

[0074]

[0075] The total energy of TDs is limited, and the energy consumed by TDs should meet the following requirements:

[0076] E kcomp +E ktr ≤E k (13)

[0077] The UAV calculation time is:

[0078]

[0079] UAV calculation time should be less than UAV flight time and should meet the following requirements:

[0080] T uco ≤T (15)

[0081] The transmission power of the kth TDs is affected by the battery life, which is subject to the peak power P max The restrictions are:

[0082]

[0083] In this system, the UAV flies at a constant altitude. Within the flight time T, the UAV flies from the starting point to the end point. Then the trajectory of the UAV will be subject to the following constraints:

[0084] ||q u [n+1]-q u [n]||=v u [n]δ,n∈N (17)

[0085]

[0086] q u [1] = q I ,q u [N+1]=q F (19)

[0087] The UAV flies at a fixed altitude, and the flight speed is v u The flight propulsion energy consumption of the rotor UAV at the nth time slot is expressed as:

[0088]

[0089] The total energy of UAV is limited, and the energy consumption of UAV should meet the following requirements:

[0090] E ucomp +E fly ≤E u (twenty two)

[0091] Based on the above two models, a UAV safety energy efficiency maximization model is established, which includes the following steps:

[0092] Define time allocation factor, task allocation, transmission power and UAV flight trajectory as block variables;

[0093] The system energy consumption model and the channel model are combined to maximize the energy efficiency of the drone by optimizing the transmission power, task offloading decision and flight trajectory under the constraints of the system energy consumption model. The energy efficiency of the drone is the ratio between the terminal device's safe offloading of data and the drone's energy consumption during the flight time, that is:

[0094]

[0095] Defining block variables They represent the time allocation factor, the trajectory of UAV, the task allocation of TDs and the transmission power respectively. Therefore, the safety energy efficiency maximization model of UAV can be expressed as:

[0096]

[0097] st(3),(4),(8),(9),(13),(15),(16),(18),(19),(21),(22) (24a)

[0098] Step S3: Solve the task allocation, time allocation factor, terminal device transmission power and UAV flight trajectory through an alternating iterative optimization algorithm until the energy efficiency of the UAV-assisted MEC system converges, and output the optimized safety energy efficiency maximization result.

[0099] Specifically, the solution process is divided into three stages. The first stage optimizes the time allocation factor and task allocation, and solves it under the given UAV trajectory and transmission power; the second stage optimizes the transmission power through the continuous convex approximation method; the third stage optimizes the UAV flight trajectory and introduces relaxation variables. Through the alternating iterative optimization algorithm, the original problem is divided into three sub-problems. Each stage can obtain a suboptimal solution to the sub-problem. Finally, it returns to the initial stage and continuously iterates and optimizes until the convergence conditions are met to obtain the optimal solution. In general, the security and energy efficiency optimization method effectively improves the performance of the communication system.

[0100] The UAV safety energy efficiency maximization model, that is, the original problem (P1) is a non-convex optimization problem, and the energy efficiency maximization can be transformed into (P2):

[0101]

[0102] st(3),(4),(8),(9),(13),(15),(16),(18),(19),(21),(22) (26a)

[0103] In the first stage, the initialization parameters of step S1 are used for iterative optimization to find the optimal result. Given the UAV trajectory Q and the transmission power P, the time allocation factor Γ and the task allocation L are solved. The original problem (P2) can be restated as problem (P2.1). Problem (P2.1) is a convex optimization problem and can be solved directly using the CVX tool in Matlab.

[0104] In the second stage, given the optimized time allocation factor Γ, the task allocation L and the initialized UAV trajectory Q, (26) and (26a) are then combined to form the non-convex expression in (9): By converting to a convex expression using the SCA method, the original problem (P2) can be restated as problem (P2.2), which can be solved directly using the CVX tool in Matlab.

[0105] The SCA method (Continuous Convex Approximation) includes:

[0106] If f(x) is a concave function, first determine an initial point x0, and perform a first-order Taylor expansion at the initial point x0 to obtain the global upper bound of f(x), expressed as:

[0107]

[0108] After the first-order Taylor expansion, the function is a linear function of x, so the function It is a convex function, and can be used in subsequent processing To approximate the original function f(x).

[0109] In the third stage, we first give the optimized time allocation factor Γ, task allocation L and TDs transmission power P, and then introduce the slack variable And meet

[0110]

[0111] The non-convex expression in (9) is By converting to a convex expression using the SCA method, the original problem (P2) can be restated as problem (P2.3), which can be solved directly using the CVX tool in Matlab.

[0112] The task allocation, time allocation factor, terminal device transmission power and UAV flight trajectory are solved by alternating iterative optimization algorithm until the UAV safety energy efficiency converges, that is, the difference between the maximum value of the system safety energy efficiency of the current iteration and the maximum value of the system safety energy efficiency of the previous iteration is less than the convergence value or the maximum number of iterations is reached. Let φ = minψ and calculate φ (r+1) The optimal value of , updates the number of iterations, and reinitializes the variables When |φ (r+1) -φ (r) |<θorr>R max , then exit the loop iteration. If the output system safety energy efficiency converges, then output the optimized maximum safety energy efficiency result. Otherwise, continue iterative optimization until the maximum safety energy efficiency of the system is obtained after convergence.

[0113] The implementation method of the safety energy efficiency optimization method of the present invention is as follows Figure 2 The performance of this method is verified in conjunction with a specific example. 2 K = 4 TDs are distributed in the geographical area, and the UAV flight altitude is H = 50m. Table 1 lists other parameter settings.

[0114] Table 1: Parameter settings

[0115] Notations value Notations value <![CDATA[U tip ]]> 200m / s T 50s ε <![CDATA[1.225kg / m 3 ]]> <![CDATA[P max ]]> 0.2W s 0.01 <![CDATA[v max ]]> 30m / s M <![CDATA[0.79m 2 ]]> <![CDATA[β0]]> -30dB <![CDATA[P o ]]> 29.03W <![CDATA[σ 2 ]]> -60dBm <![CDATA[P i ]]> 79.07W B 50MHz <![CDATA[v o ]]> 3.6lm / s <![CDATA[C u ]]> 500cycle / bit <![CDATA[d o ]]> 0.075 <![CDATA[f u ]]> 2GHz N 100 <![CDATA[C k ]]> 200cycle / bit <![CDATA[E k ]]> 5J <![CDATA[к u ]]> <![CDATA[10 -28 ]]> <![CDATA[κ k ]]> <![CDATA[10 -28 ]]> <![CDATA[L k ]]> 100Mbit <![CDATA[f k ]]> 1GHz <![CDATA[E u ]]> 2400J <![CDATA[q I ]]> [0，0]m δ 0.5s <![CDATA[q F ]]> [1000，0]m <![CDATA[w e ]]> [600，400]m <![CDATA[w1]]> [200，350]m <![CDATA[w2]]> [600，750]m <![CDATA[w3]]> [450，850]m <![CDATA[w4]]> [800，350]m θ 0.0001 <![CDATA[R max ]]> 30

[0116] The present invention jointly optimizes the unloading time allocation, UAV trajectory, TDs computing task allocation and transmission power, and establishes a UAV security energy efficiency maximization model under the constraints of confidentiality unloading constraints, TDs and UAV energy consumption, optimizes the energy consumption of UAV, and optimizes the system confidentiality unloading rate, improves the service quality of the application, expands the computing power of TDs, and enhances the performance of the communication system. At the same time, robust secure communication under maximum energy efficiency is considered to effectively enhance the security communication performance of the system.

[0117] Based on the same inventive concept, the present application provides a drone control system, including

[0118] Rotary wing drones;

[0119] At least one ground terminal device (TDs);

[0120] Mobile Edge Computing (MEC) servers;

[0121] A control unit is used to implement any of the safety energy efficiency optimization methods described above.

[0122] Based on the same inventive concept, the present invention further provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the aforementioned method are implemented.

[0123] This application can be applied to energy storage battery management and energy management systems (EMS) in outdoor and mobile energy storage products to achieve more efficient power allocation and management under limited power resources and improve the efficiency of energy storage equipment. In addition, outdoor portable energy storage products and intelligent battery management systems may face the risk of eavesdropping on wireless channels during data transmission. This method optimization method can help improve data confidentiality during the communication process, ensure data security of energy storage devices when connected to the network, and reduce the possibility of information leakage.

[0124] Among them, the computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form, etc. Computer readable media may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in computer readable media can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer readable media do not include electric carrier signals and telecommunication signals.

[0125] The present invention can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0126] The present application also provides an electronic device, comprising a memory, multiple processors, and a program stored in the memory, wherein the program is configured to be executed by the processors, and the steps of the above method are implemented when the multiple processors execute the program.

[0127] In addition, the present invention also provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the aforementioned method are implemented. The present invention can be used in many general or special computing system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0128] The device in this embodiment and the method in the aforementioned embodiment are two aspects based on the same inventive concept. The method implementation process has been described in detail above, so those skilled in the art can clearly understand the structure and implementation process of the system in this embodiment based on the aforementioned description. For the sake of brevity of the specification, they will not be repeated here.

[0129] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A safety energy efficiency optimization method, characterized in that: The following steps are involved: Initialize the UAV flight parameters; Establishing a system channel model, an energy consumption model and a UAV safety energy efficiency maximization model according to the UAV flight parameters; The task allocation, time allocation factor, terminal device transmission power and UAV flight trajectory are solved through an alternating iterative optimization algorithm until the UAV safety and energy efficiency converges, and the optimized UAV safety and energy efficiency maximization result is output.

2. The safety energy efficiency optimization method according to claim 1, characterized in that: Establishing the system channel model includes: Determine the channel power gain between the UAV and the terminal device, and the channel power gain between the terminal device and the ground eavesdropper; Define the time allocation factor and determine the constraints of the time allocation factor; Calculate the rate between the terminal device and the drone, the rate between the terminal device and the ground eavesdropper, and the confidentiality rate, and build a system channel model.

3. The safety energy efficiency optimization method according to claim 1, characterized in that: Establishing a system energy consumption model includes: Determine the mission energy consumption, wherein the mission energy consumption includes computing mission energy consumption, communication mission energy consumption, and flight propulsion energy consumption; The energy consumption constraints of each task are determined according to the task energy consumption, and the energy consumption model required for the UAV transmission power, task allocation and flight trajectory optimization is constructed according to the energy consumption constraints.

4. The safety energy efficiency optimization method according to claim 1, characterized in that: Establish a UAV safety and energy efficiency maximization model, including: Define time allocation factor, task allocation, transmission power and UAV flight trajectory as block variables; The system energy consumption model and the channel model are combined to maximize the energy efficiency of the UAV by optimizing the transmission power, task offloading decision and flight trajectory under the constraints of the system energy consumption model; the energy efficiency of the UAV is the ratio between the terminal device's safe offloading of data and the energy consumption of the UAV during the flight time.

5. The safety energy efficiency optimization method according to claim 1, characterized in that: The task allocation, time allocation factor, terminal device transmission power and UAV flight trajectory are solved through an alternating iterative optimization algorithm, including: The first stage optimizes the time allocation factor and task allocation, given the trajectory and transmission power of the UAV; The second stage optimizes the transmit power by a continuous convex approximation method; The third stage optimizes the UAV flight trajectory and introduces relaxation variables.

6. The safety energy efficiency optimization method according to claim 5, characterized in that: Through the time allocation factor and task allocation in the first stage and the transmission power in the second stage, slack variables are introduced in the UAV confidentiality rate and flight energy consumption. The third stage problem is solved by the continuous convex approximation method to optimize the UAV trajectory.

7. The safety energy efficiency optimization method according to claim 1, characterized in that: Based on drone-assisted MEC system safety rate convergence, including: The difference between the maximum energy efficiency of the current iteration and the maximum energy efficiency of the previous iteration is less than the preset convergence value, or the maximum number of iterations is reached.

8. A drone control system, characterized in that: include Rotary wing drones; at least one ground terminal device; Mobile edge computing server; A control unit, used to implement the safety energy efficiency optimization method as described in any one of claims 1 to 7.

9. A storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the safety energy efficiency optimization method according to any one of claims 1 to 7 is implemented.

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